Overview

Dataset statistics

Number of variables13
Number of observations1000
Missing cells95
Missing cells (%)0.7%
Duplicate rows33
Duplicate rows (%)3.3%
Total size in memory101.7 KiB
Average record size in memory104.1 B

Variable types

Text5
Numeric4
Categorical4

Alerts

Dataset has 33 (3.3%) duplicate rowsDuplicates
km_driven is highly overall correlated with yearHigh correlation
selling_price is highly overall correlated with transmission and 1 other fieldsHigh correlation
transmission is highly overall correlated with selling_priceHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (52.7%)Imbalance
mileage has 19 (1.9%) missing valuesMissing
engine has 19 (1.9%) missing valuesMissing
max_power has 19 (1.9%) missing valuesMissing
torque has 19 (1.9%) missing valuesMissing
seats has 19 (1.9%) missing valuesMissing

Reproduction

Analysis started2025-11-30 13:12:45.122969
Analysis finished2025-11-30 13:12:46.001979
Duration0.88 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

name
Text

Distinct621
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-11-30T16:12:46.110541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length24.857
Min length11

Characters and Unicode

Total characters24857
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)44.0%

Sample

1st rowMahindra Xylo E4 BS IV
2nd rowTata Nexon 1.5 Revotorq XE
3rd rowHonda Civic 1.8 S AT
4th rowHonda City i DTEC VX
5th rowTata Indica Vista Aura 1.2 Safire BSIV
ValueCountFrequency (%)
maruti290
 
6.2%
hyundai198
 
4.2%
tata106
 
2.3%
mahindra90
 
1.9%
swift83
 
1.8%
diesel83
 
1.8%
bsiv79
 
1.7%
vxi74
 
1.6%
1.271
 
1.5%
plus64
 
1.4%
Other values (495)3549
75.7%
2025-11-30T16:12:46.270363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3687
 
14.8%
a1852
 
7.5%
i1631
 
6.6%
t1253
 
5.0%
r1094
 
4.4%
o1010
 
4.1%
n934
 
3.8%
e890
 
3.6%
u738
 
3.0%
S701
 
2.8%
Other values (57)11067
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)24857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a1852
 
7.5%
i1631
 
6.6%
t1253
 
5.0%
r1094
 
4.4%
o1010
 
4.1%
n934
 
3.8%
e890
 
3.6%
u738
 
3.0%
S701
 
2.8%
Other values (57)11067
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a1852
 
7.5%
i1631
 
6.6%
t1253
 
5.0%
r1094
 
4.4%
o1010
 
4.1%
n934
 
3.8%
e890
 
3.6%
u738
 
3.0%
S701
 
2.8%
Other values (57)11067
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a1852
 
7.5%
i1631
 
6.6%
t1253
 
5.0%
r1094
 
4.4%
o1010
 
4.1%
n934
 
3.8%
e890
 
3.6%
u738
 
3.0%
S701
 
2.8%
Other values (57)11067
44.5%

year
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.681
Minimum1995
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-30T16:12:46.302117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0121486
Coefficient of variation (CV)0.001992445
Kurtosis1.2158841
Mean2013.681
Median Absolute Deviation (MAD)3
Skewness-1.0223557
Sum2013681
Variance16.097336
MonotonicityNot monotonic
2025-11-30T16:12:46.331905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2017134
13.4%
2016106
10.6%
201596
9.6%
201891
9.1%
201185
8.5%
201283
8.3%
201479
7.9%
201376
7.6%
201964
6.4%
201049
 
4.9%
Other values (14)137
13.7%
ValueCountFrequency (%)
19951
 
0.1%
19981
 
0.1%
19995
 
0.5%
20001
 
0.1%
20012
 
0.2%
20024
 
0.4%
20038
 
0.8%
200410
1.0%
200510
1.0%
200620
2.0%
ValueCountFrequency (%)
20204
 
0.4%
201964
6.4%
201891
9.1%
2017134
13.4%
2016106
10.6%
201596
9.6%
201479
7.9%
201376
7.6%
201283
8.3%
201185
8.5%

selling_price
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617901.04
Minimum31000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-30T16:12:46.363798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31000
5-th percentile100000
Q1250000
median434999
Q3670000
95-th percentile1904049
Maximum6000000
Range5969000
Interquartile range (IQR)420000

Descriptive statistics

Standard deviation758553.86
Coefficient of variation (CV)1.22763
Kurtosis21.438457
Mean617901.04
Median Absolute Deviation (MAD)205000
Skewness4.2148309
Sum6.1790104 × 108
Variance5.7540396 × 1011
MonotonicityNot monotonic
2025-11-30T16:12:46.400315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000029
 
2.9%
35000028
 
2.8%
60000028
 
2.8%
55000025
 
2.5%
65000024
 
2.4%
40000024
 
2.4%
25000022
 
2.2%
50000022
 
2.2%
75000022
 
2.2%
45000016
 
1.6%
Other values (264)760
76.0%
ValueCountFrequency (%)
310001
 
0.1%
339831
 
0.1%
350001
 
0.1%
400001
 
0.1%
450005
0.5%
460001
 
0.1%
500002
 
0.2%
520002
 
0.2%
550003
0.3%
555991
 
0.1%
ValueCountFrequency (%)
60000002
 
0.2%
55000005
0.5%
54000002
 
0.2%
51500003
 
0.3%
41000001
 
0.1%
38000002
 
0.2%
37500001
 
0.1%
34000001
 
0.1%
32510001
 
0.1%
32000008
0.8%

km_driven
Real number (ℝ)

High correlation 

Distinct260
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71393.341
Minimum1303
Maximum375000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-30T16:12:46.433846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1303
5-th percentile9190
Q137000
median61500
Q3100000
95-th percentile160000
Maximum375000
Range373697
Interquartile range (IQR)63000

Descriptive statistics

Standard deviation48486.219
Coefficient of variation (CV)0.67914203
Kurtosis3.8337561
Mean71393.341
Median Absolute Deviation (MAD)28500
Skewness1.4228571
Sum71393341
Variance2.3509134 × 109
MonotonicityNot monotonic
2025-11-30T16:12:46.470079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000066
 
6.6%
7000058
 
5.8%
6000055
 
5.5%
8000054
 
5.4%
4000046
 
4.6%
5000044
 
4.4%
9000038
 
3.8%
11000035
 
3.5%
10000033
 
3.3%
3000027
 
2.7%
Other values (250)544
54.4%
ValueCountFrequency (%)
13031
 
0.1%
20007
0.7%
23881
 
0.1%
26001
 
0.1%
31001
 
0.1%
35002
 
0.2%
35641
 
0.1%
40001
 
0.1%
43371
 
0.1%
50009
0.9%
ValueCountFrequency (%)
3750001
0.1%
3000002
0.2%
2980001
0.1%
2910001
0.1%
2700001
0.1%
2650001
0.1%
2640001
0.1%
2600001
0.1%
2500001
0.1%
2480001
0.1%

fuel
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Diesel
534 
Petrol
457 
CNG
 
5
LPG
 
4

Length

Max length6
Median length6
Mean length5.973
Min length3

Characters and Unicode

Total characters5973
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel534
53.4%
Petrol457
45.7%
CNG5
 
0.5%
LPG4
 
0.4%

Length

2025-11-30T16:12:46.503748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T16:12:46.523528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel534
53.4%
petrol457
45.7%
cng5
 
0.5%
lpg4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Individual
837 
Dealer
135 
Trustmark Dealer
 
28

Length

Max length16
Median length10
Mean length9.628
Min length6

Characters and Unicode

Total characters9628
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual837
83.7%
Dealer135
 
13.5%
Trustmark Dealer28
 
2.8%

Length

2025-11-30T16:12:46.547971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T16:12:46.565883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual837
81.4%
dealer163
 
15.9%
trustmark28
 
2.7%

Most occurring characters

ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
v837
8.7%
n837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
v837
8.7%
n837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
v837
8.7%
n837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
v837
8.7%
n837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Manual
877 
Automatic
123 

Length

Max length9
Median length6
Mean length6.369
Min length6

Characters and Unicode

Total characters6369
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowAutomatic
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual877
87.7%
Automatic123
 
12.3%

Length

2025-11-30T16:12:46.590266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T16:12:46.607873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual877
87.7%
automatic123
 
12.3%

Most occurring characters

ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

owner
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
First Owner
623 
Second Owner
278 
Third Owner
71 
Fourth & Above Owner
 
27
Test Drive Car
 
1

Length

Max length20
Median length11
Mean length11.524
Min length11

Characters and Unicode

Total characters11524
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFirst Owner
2nd rowFirst Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowSecond Owner

Common Values

ValueCountFrequency (%)
First Owner623
62.3%
Second Owner278
27.8%
Third Owner71
 
7.1%
Fourth & Above Owner27
 
2.7%
Test Drive Car1
 
0.1%

Length

2025-11-30T16:12:46.629996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T16:12:46.649852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
owner999
48.6%
first623
30.3%
second278
 
13.5%
third71
 
3.5%
fourth27
 
1.3%
27
 
1.3%
above27
 
1.3%
test1
 
< 0.1%
drive1
 
< 0.1%
car1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

mileage
Text

Missing 

Distinct237
Distinct (%)24.2%
Missing19
Missing (%)1.9%
Memory size7.9 KiB
2025-11-30T16:12:46.752393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.4057085
Min length8

Characters and Unicode

Total characters9227
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)7.4%

Sample

1st row14.0 kmpl
2nd row21.5 kmpl
3rd row12.9 kmpl
4th row25.1 kmpl
5th row16.5 kmpl
ValueCountFrequency (%)
kmpl972
49.5%
18.623
 
1.2%
18.922
 
1.1%
21.122
 
1.1%
19.721
 
1.1%
16.117
 
0.9%
17.016
 
0.8%
12.816
 
0.8%
18.215
 
0.8%
22.7415
 
0.8%
Other values (225)823
41.9%
2025-11-30T16:12:46.888721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k990
10.7%
.981
10.6%
981
10.6%
m981
10.6%
p972
10.5%
l972
10.5%
1783
8.5%
2652
7.1%
0271
 
2.9%
5251
 
2.7%
Other values (8)1393
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)9227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k990
10.7%
.981
10.6%
981
10.6%
m981
10.6%
p972
10.5%
l972
10.5%
1783
8.5%
2652
7.1%
0271
 
2.9%
5251
 
2.7%
Other values (8)1393
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k990
10.7%
.981
10.6%
981
10.6%
m981
10.6%
p972
10.5%
l972
10.5%
1783
8.5%
2652
7.1%
0271
 
2.9%
5251
 
2.7%
Other values (8)1393
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k990
10.7%
.981
10.6%
981
10.6%
m981
10.6%
p972
10.5%
l972
10.5%
1783
8.5%
2652
7.1%
0271
 
2.9%
5251
 
2.7%
Other values (8)1393
15.1%

engine
Text

Missing 

Distinct88
Distinct (%)9.0%
Missing19
Missing (%)1.9%
Memory size7.9 KiB
2025-11-30T16:12:46.956692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8236493
Min length6

Characters and Unicode

Total characters6694
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.9%

Sample

1st row2498 CC
2nd row1497 CC
3rd row1799 CC
4th row1498 CC
5th row1172 CC
ValueCountFrequency (%)
cc981
50.0%
1248116
 
5.9%
1197105
 
5.4%
79663
 
3.2%
99857
 
2.9%
139651
 
2.6%
217949
 
2.5%
149847
 
2.4%
249432
 
1.6%
119931
 
1.6%
Other values (79)430
21.9%
2025-11-30T16:12:47.047606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C1962
29.3%
981
14.7%
1959
14.3%
9855
12.8%
4386
 
5.8%
8366
 
5.5%
2345
 
5.2%
7290
 
4.3%
6223
 
3.3%
3147
 
2.2%
Other values (2)180
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C1962
29.3%
981
14.7%
1959
14.3%
9855
12.8%
4386
 
5.8%
8366
 
5.5%
2345
 
5.2%
7290
 
4.3%
6223
 
3.3%
3147
 
2.2%
Other values (2)180
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C1962
29.3%
981
14.7%
1959
14.3%
9855
12.8%
4386
 
5.8%
8366
 
5.5%
2345
 
5.2%
7290
 
4.3%
6223
 
3.3%
3147
 
2.2%
Other values (2)180
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C1962
29.3%
981
14.7%
1959
14.3%
9855
12.8%
4386
 
5.8%
8366
 
5.5%
2345
 
5.2%
7290
 
4.3%
6223
 
3.3%
3147
 
2.2%
Other values (2)180
 
2.7%

max_power
Text

Missing 

Distinct182
Distinct (%)18.6%
Missing19
Missing (%)1.9%
Memory size7.9 KiB
2025-11-30T16:12:47.133609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.7787971
Min length6

Characters and Unicode

Total characters7631
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)5.6%

Sample

1st row112 bhp
2nd row108.5 bhp
3rd row130 bhp
4th row98.6 bhp
5th row65 bhp
ValueCountFrequency (%)
bhp981
50.0%
7443
 
2.2%
88.528
 
1.4%
47.324
 
1.2%
81.8024
 
1.2%
67.122
 
1.1%
46.321
 
1.1%
88.7320
 
1.0%
88.720
 
1.0%
7019
 
1.0%
Other values (173)760
38.7%
2025-11-30T16:12:47.324545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
981
12.9%
b981
12.9%
h981
12.9%
p981
12.9%
.617
8.1%
8546
7.2%
1448
5.9%
7422
 
5.5%
6308
 
4.0%
3278
 
3.6%
Other values (5)1088
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)7631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
981
12.9%
b981
12.9%
h981
12.9%
p981
12.9%
.617
8.1%
8546
7.2%
1448
5.9%
7422
 
5.5%
6308
 
4.0%
3278
 
3.6%
Other values (5)1088
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
981
12.9%
b981
12.9%
h981
12.9%
p981
12.9%
.617
8.1%
8546
7.2%
1448
5.9%
7422
 
5.5%
6308
 
4.0%
3278
 
3.6%
Other values (5)1088
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
981
12.9%
b981
12.9%
h981
12.9%
p981
12.9%
.617
8.1%
8546
7.2%
1448
5.9%
7422
 
5.5%
6308
 
4.0%
3278
 
3.6%
Other values (5)1088
14.3%

torque
Text

Missing 

Distinct226
Distinct (%)23.0%
Missing19
Missing (%)1.9%
Memory size7.9 KiB
2025-11-30T16:12:47.436320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length25
Mean length16.293578
Min length5

Characters and Unicode

Total characters15984
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)9.1%

Sample

1st row260 Nm at 1800-2200 rpm
2nd row260Nm@ 1500-2750rpm
3rd row172Nm@ 4300rpm
4th row200Nm@ 1750rpm
5th row96 Nm at 3000 rpm
ValueCountFrequency (%)
4000rpm114
 
5.5%
3500rpm97
 
4.7%
200nm89
 
4.3%
2000rpm83
 
4.0%
1750rpm69
 
3.3%
190nm67
 
3.2%
rpm63
 
3.0%
90nm52
 
2.5%
3000rpm39
 
1.9%
2500rpm39
 
1.9%
Other values (246)1373
65.9%
2025-11-30T16:12:47.576144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
03273
20.5%
m1949
12.2%
1109
 
6.9%
11054
 
6.6%
@990
 
6.2%
r973
 
6.1%
p973
 
6.1%
N895
 
5.6%
2860
 
5.4%
5809
 
5.1%
Other values (23)3099
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)15984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03273
20.5%
m1949
12.2%
1109
 
6.9%
11054
 
6.6%
@990
 
6.2%
r973
 
6.1%
p973
 
6.1%
N895
 
5.6%
2860
 
5.4%
5809
 
5.1%
Other values (23)3099
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03273
20.5%
m1949
12.2%
1109
 
6.9%
11054
 
6.6%
@990
 
6.2%
r973
 
6.1%
p973
 
6.1%
N895
 
5.6%
2860
 
5.4%
5809
 
5.1%
Other values (23)3099
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03273
20.5%
m1949
12.2%
1109
 
6.9%
11054
 
6.6%
@990
 
6.2%
r973
 
6.1%
p973
 
6.1%
N895
 
5.6%
2860
 
5.4%
5809
 
5.1%
Other values (23)3099
19.4%

seats
Real number (ℝ)

Missing 

Distinct6
Distinct (%)0.6%
Missing19
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean5.4108053
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-30T16:12:47.604817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum9
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91998528
Coefficient of variation (CV)0.17002742
Kurtosis1.7775742
Mean5.4108053
Median Absolute Deviation (MAD)0
Skewness1.6424577
Sum5308
Variance0.84637292
MonotonicityNot monotonic
2025-11-30T16:12:47.628266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5758
75.8%
7161
 
16.1%
424
 
2.4%
823
 
2.3%
68
 
0.8%
97
 
0.7%
(Missing)19
 
1.9%
ValueCountFrequency (%)
424
 
2.4%
5758
75.8%
68
 
0.8%
7161
 
16.1%
823
 
2.3%
97
 
0.7%
ValueCountFrequency (%)
97
 
0.7%
823
 
2.3%
7161
 
16.1%
68
 
0.8%
5758
75.8%
424
 
2.4%

Interactions

2025-11-30T16:12:45.654121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.274444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.399404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.525312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.685974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.306374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.431625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.557688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.715526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.337965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.462334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.590113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.858590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.370169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.495338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T16:12:45.623366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-30T16:12:47.651021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
fuelkm_drivenownerseatsseller_typeselling_pricetransmissionyear
fuel1.0000.1740.0000.2200.1060.1500.0000.133
km_driven0.1741.0000.1640.2460.142-0.3280.243-0.597
owner0.0000.1641.0000.0630.1740.1650.1470.281
seats0.2200.2460.0631.0000.0280.2910.0390.016
seller_type0.1060.1420.1740.0281.0000.3640.3620.196
selling_price0.150-0.3280.1650.2910.3641.0000.6280.710
transmission0.0000.2430.1470.0390.3620.6281.0000.308
year0.133-0.5970.2810.0160.1960.7100.3081.000

Missing values

2025-11-30T16:12:45.903553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-30T16:12:45.938819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-30T16:12:45.979740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
0Mahindra Xylo E4 BS IV2010229999168000DieselIndividualManualFirst Owner14.0 kmpl2498 CC112 bhp260 Nm at 1800-2200 rpm7.0
1Tata Nexon 1.5 Revotorq XE201766500025000DieselIndividualManualFirst Owner21.5 kmpl1497 CC108.5 bhp260Nm@ 1500-2750rpm5.0
2Honda Civic 1.8 S AT2007175000218463PetrolIndividualAutomaticFirst Owner12.9 kmpl1799 CC130 bhp172Nm@ 4300rpm5.0
3Honda City i DTEC VX2015635000173000DieselIndividualManualFirst Owner25.1 kmpl1498 CC98.6 bhp200Nm@ 1750rpm5.0
4Tata Indica Vista Aura 1.2 Safire BSIV201113000070000PetrolIndividualManualSecond Owner16.5 kmpl1172 CC65 bhp96 Nm at 3000 rpm5.0
5Mahindra Thar CRDe201997500012584DieselDealerManualFirst Owner16.55 kmpl2498 CC105 bhp247Nm@ 1800-2000rpm6.0
6Chevrolet Spark 1.0 LS201115000035000PetrolIndividualManualFirst Owner18.0 kmpl995 CC62 bhp90.3Nm@ 4200rpm5.0
7Maruti Ritz ZXi201227500070000PetrolIndividualManualSecond Owner18.5 kmpl1197 CC85.80 bhp114Nm@ 4000rpm5.0
8Maruti Alto LX201114000072000PetrolIndividualManualSecond Owner19.7 kmpl796 CC46.3 bhp62Nm@ 3000rpm5.0
9Hyundai Creta 1.6 CRDi SX201685000058000DieselIndividualManualFirst Owner19.67 kmpl1582 CC126.2 bhp259.9Nm@ 1900-2750rpm5.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
990Maruti Alto LXi20079500070000PetrolIndividualManualSecond Owner19.7 kmpl796 CC46.3 bhp62Nm@ 3000rpm5.0
991Honda Brio V MT201237600026000PetrolIndividualManualFirst Owner19.4 kmpl1198 CC86.8 bhp109Nm@ 4500rpm5.0
992Maruti Alto LXi200685000150000PetrolIndividualManualSecond Owner19.7 kmpl796 CC46.3 bhp62Nm@ 3000rpm5.0
993Maruti 800 DX199952000100000PetrolIndividualManualFirst Owner16.1 kmpl796 CC37 bhp59Nm@ 2500rpm4.0
994Maruti Swift Dzire VXi2010240000143000PetrolIndividualManualFirst Owner17.5 kmpl1298 CC85.8 bhp114Nm@ 4000rpm5.0
995Hyundai i10 Magna 1.1L2008250000100000PetrolIndividualManualSecond Owner19.81 kmpl1086 CC68.05 bhp99.04Nm@ 4500rpm5.0
996Hyundai i20 2015-2017 Sportz 1.2201744000050000PetrolIndividualManualSecond Owner18.6 kmpl1197 CC81.83 bhp114.7Nm@ 4000rpm5.0
997Hyundai i20 Era Diesel200934000040000DieselIndividualManualFirst Owner23.0 kmpl1396 CC90 bhp22.4 kgm at 1750-2750rpm5.0
998Hyundai i10 Asta201235000025000PetrolIndividualManualFirst Owner20.36 kmpl1197 CC78.9 bhp111.8Nm@ 4000rpm5.0
999Honda City i DTec SV2016700000110000DieselIndividualManualFirst Owner26.0 kmpl1498 CC98.6 bhp200Nm@ 1750rpm5.0

Duplicate rows

Most frequently occurring

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats# duplicates
2Honda Jazz VX201655000056494PetrolTrustmark DealerManualFirst Owner18.2 kmpl1199 CC88.7 bhp110Nm@ 4800rpm5.08
9Jaguar XF 2.0 Diesel Portfolio2017320000045000DieselDealerAutomaticFirst Owner19.33 kmpl1999 CC177 bhp430Nm@ 1750-2500rpm5.06
28Toyota Camry 2.5 Hybrid2016200000068089PetrolTrustmark DealerAutomaticFirst Owner19.16 kmpl2494 CC157.7 bhp213Nm@ 4500rpm5.06
31Volvo V40 D3 R-Design201824750002000DieselDealerAutomaticFirst Owner16.8 kmpl1984 CC150 bhp350Nm@ 1500-2750rpm5.06
1BMW X4 M Sport X xDrive20d201955000008500DieselDealerAutomaticFirst Owner16.78 kmpl1995 CC190 bhp400Nm@ 1750-2500rpm5.04
17Maruti Swift AMT VVT VXI20196500005621PetrolTrustmark DealerAutomaticFirst Owner22.0 kmpl1197 CC81.80 bhp113Nm@ 4200rpm5.04
23Skoda Rapid 1.6 MPI AT Elegance201664500011000PetrolDealerAutomaticFirst Owner14.3 kmpl1598 CC103.5 bhp153Nm@ 3800rpm5.04
25Tata Safari Storme EX2015503000110000DieselIndividualManualFirst Owner14.1 kmpl2179 CC147.94 bhp320Nm@ 1500-3000rpm7.04
4Hyundai Grand i10 1.2 CRDi Sportz201745000056290DieselDealerManualFirst Owner24.0 kmpl1186 CC73.97 bhp190.24nm@ 1750-2250rpm5.03
10Lexus ES 300h2019515000020000PetrolDealerAutomaticFirst Owner22.37 kmpl2487 CC214.56 bhp202Nm@ 3600-5200rpm5.03